Int. J. Pharm. Sci. Rev. Res., 15(1), 2012; nᵒ 17, 83-87
ISSN 0976 – 044X
Research Article
STUDY OF PLANTGLUCOSYLXANTHONE & ITS ANALOGS, AS DPPIV INHIBITOR
FOR ANTI-DIABETIC ACTIVITY
1
2
1
1
Pavan Kumar K.V.T.S *, Rama koteswararao.Chinta , Shriram Raghavan , P.M. Murali
Department of Plant Biotechnology, Dalmia Centre for Research & Development, Coimbatore, India.
2
Department of Chemistry, College of Engineering, Jawaharlal Nehru Technological University, Kukatpally, Hyderabad, India.
*Corresponding author’s E-mail: kvtspavan@gmail.com
1
Accepted on: 18-05-2012; Finalized on: 30-06-2012.
ABSTRACT
Diabetes Mellitus, One of the life style disorders, has become common problem in current world scenario and creating health
“Tsunami” due to un-availability of cost effective treatment. The role of Dipeptidyl peptidase IV receptor, an anti-diabetic drug
target, involved in incretin metabolism has provided an opportunity to counteract type2 diabetes by inhibiting its action on glucagon
like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). Plants are one the major sources for natural
products with various therapeutic activities and ancient Indian traditional medicine Ayurveda uses many of these plant ingredients
to treat several human ailments. A Xanthone class compound, glucosylxanthone, is one of such natural compound from plant that
has various biological activities. In the current effort glucosylxanthone and its analogues are studied for insilico inhibitory activity of
DPPIV receptor along with known DPPIV inhibitors that includes molecules in various phases of drug discovery pipeline by using
MOLA, tool for Virtual Screening using AutoDock4/Vina in a computer cluster using non-dedicated multi-platform computers.
Glucosylxanthone and its analogues have shown good comparable insilico binding activity with FDA approved drugs and other
molecules currently under development for DPPIV inhibitory activity as anti-diabetic therapy.
Keywords: Diabetes, Natural compounds, Xanthones, MOLA, Autodock vina, Virtual Screening, DPPIV.
INTRODUCTION
The multifactorial metabolic disorder, Diabetes Mellitus1,
is a chronic condition that occurs due to inability of the
body to produce enough or effectively use insulin, has
become common disorder. Out of three main types of
Diabetes2, NIDDM - Non Insulin Dependent Diabetes
Mellitus occurs primarily due to defects in insulin
secretion along with development of insulin resistance.
The current available treatments for diabetes with insulin
therapy including oral insulin are not cost effective and
oral drugs that enhance insulin secretion showed
undesirable side effects like hypoglycemia, cardiovascular
abnormalities and pancreatic beta-cell apoptosis, failed to
address the problem3. 5th edition of the Diabetes atlas
released by International Diabetes federation (IDF)
indicates people living with diabetes are expected to rise
from 366 million in 2011 to 552 million in 20304. The
deadly disease caused 4.6 million deaths in 2011 with at
least USD 465 billion dollars healthcare expenditures in
2011 which is 11% of total health care expenditures in
adults (20-79 years), is undoubtedly one of the
challenging health problems in 21st century and is going
to create a health Tsunami if proper action is not taken.
Population-based diabetes studies shows many people
remain undiagnosed due to few symptoms or symptoms
may not be recognized related to diabetes. The role of
DPP-IV inhibitor (Di Peptidyl Peptidase-IV) in incretin
metabolism that prolongs in-vivo half-life of glucagon like
peptide-1 (GLP-1) and glucose-dependent insulinotropic
polypeptide (GIP) from DPP-IV there by increased insulin
gene expression, beta-cell proliferation, islets neo-genesis
leading to the proposal that the DPP-IV inhibitors as oral
drugs for treatment of Type-2 diabetes5 and was
supported by experimental results in DPP-IV knockout
mice, diabetes mice models treated with DPP-IV
inhibitors6,7. Though companies like Merck, Takeda
developed molecules that belong to gliptin class of
chemical compounds and several other companies are
working on this target (table 1) there is no single
molecule from natural source.
Table 1: DPPIV inhibitors - approved & under
development
Drug Name
Company
Year of FDA
approval
Sitagliptin
Vildagliptin
Merck
Novartis
2006
2008
Saxagliptin
Alogliptin
Linagliptin
Dutogliptin
Teneligliptin
BMS
Takeda
Eli Lilly
Phenomix
Mitsubishi Tanabe Pharma Corp
2009
2010
2011
Phase III
Phase III
SYR 472
KRP104
Melogliptin
Takeda
Kyorin
Glenmark
Phase III
Phase II
Phase II
Plants, one of the major sources of natural products with
numerous therapeutic activities shows good efficacy with
better safety profile and still remains unexplored to full
extent. Varying degree of hypoglycemic and antihyperglycemic activity of plants (table 2) and the
mechanism of action for these plant based formulations
were not clearly understood. One of the world’s ancient
medicines, Indian traditional medicine Ayurveda uses
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Int. J. Pharm. Sci. Rev. Res., 15(1), 2012; nᵒ 17, 83-87
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several of these plants to treat human ailments . Drug
discovery has reached important crossroads and
organizations started realizing the importance of the
approaches of ancient medicine and started focusing on
natural products which have become a major source for
lead molecules in current drug discovery efforts.
Figure 1: Plant based glucosylxanthone & its analogues
ISSN 0976 – 044X
leaves, bark of some traditional Indian medicinal plants
(table 3) and has shown several therapeutic activities
including antioxidant, anti-diabetic, antihypertensive,
anti-HIV, anticancer, analgesic, hepatoprotective,
9
immunomodulatory properties .
Table 3: Indian
Glucosylxanthone
Medicinal
Plant Name
Anemarrenaasphodeloides
Bombaxmalabaricum
Cyclopiagenistoides
Cyclopiamaculata
Cyclopiasubternata
Gnidiainvolucrata
Hypericumsampsonii
Salaciachinensis
Swertiacorymbosa
Swertiapunctata
plants
Producing
Plant Name
Arrabidaeasamydoides
Cratoxylumcochinchinense
Cyclopiaintermedia
Cyclopiasessiliflora
Gentianalutea
Hypericumperforatum
Mangifera indica
Salaciareticulata
Swertiafranchetiana
MATERIALS AND METHODS
Ligands preparation
ACD labs chemistry drawing tool Chemsketch version
12.0110 was used to draw FDA approved DPPIV inhibitory
drugs, DPPIV inhibitor molecules currently under
development, Glucosylxanthone and its analogues. These
molecules were converted to Structure data format files11
using SDF plugin tool for Chemsketch. The Structure data
files were converted to protein databank files12 having 3D
coordinates with open source tolls Balloon13& Open Babel
applications14 and energy minimized using MMF94 force
field15.
Protein preparation
Table 2: Plants with hypoglycaemic activity
Plant Name
Annonasquamosa
Areca catechu
Boerhaviadiffusa
Buteamonosperma
Capparis decidua
Centellaasiatica
Emblicaofficinalis
Enicostemalittorale
Gymnemasylvestre
Hibiscus rosa-sinesis
Momordicacymbalaria
Musa sapientum
Pinuspinaster
Salaciareticulata
Scopariadulcis
Syzygium alternifolium
Terminaliachebula
Vincarosea
Plant Name
Artemisia pallens
Beta vulgaris
Bombaxceiba
Camellia sinensis
Caesalpiniabonducella
Coccinia indica
Eugenia uniflora
Ficusbengalenesis
Hemidesmusindicus
Ipomoea batatas
Murrayakoenigii
Phaseolus vulgaris
Punicagranatum
Salvia miltiorrhiza
Swertiachirayita
Terminaliabelerica
Tinosporacrispa
Withaniasomnifera
Considering these facts as a motivation, the current study
is performed to understand the usefulness of DPP-IV
inhibitory profile of plant based glucosylxanthone & its
analogues (fig.1) using computational biology approach.
Glucosylxanthone are major components of rhizomes,
Structure of DPPIV submitted by Kim D et al., to protein
databank (PDB Code: 1X70) was obtained and employed
as a virtual target. Auto Dock Tools17 (ADT) was employed
to select and separate the bound ligand file from target
file initially downloaded from Protein Data Bank and each
structure was restored as separate PDB files. Auto dock
tools protein preparation wizard was used to prepare
protein file and resulting structure was saved as a PDBQT
file to use in docking studies. Grid maps were generated
for all possible atom types (29 atom types - A BR Br C CA
Ca CL Cl F FE Fe H HD HS I MG Mg MN Mn N NA NS OA OS
P S SA ZN Zn) as we are using many compounds. The
prepared protein-ligand complex of 1X70 was employed
to build energy grids using the default value of protein
atom scaling (1.0) within a cubic box of dimensions 30 Å X
30 Å X 30 Å centred around the centroid of the bound
ligand pose. The bounding box dimensions were set to 14
Å X 14 Å X14 Å. No constraints were imposed on any of
the active site protein atoms vis-à-vis their interactions
with ligand atoms.
Docking Studies
In order to understand the binding affinity and
interactions between protein and ligand molecules an
easy-to-use graphical user interface tool, MOLA18, which
automates parallel virtual screening using AutoDock419
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Int. J. Pharm. Sci. Rev. Res., 15(1), 2012; nᵒ 17, 83-87
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and/or Vina in bootable non-dedicated computer
clusters was used. Several tasks needed for
AutoDock4/Vina are automated with MOLA that includes
ligand preparation, AutoDock4/Vina jobs distribution,
result analysis and ligand ranking. To select the
parameters for docking study using Autodock Vina and for
handling output files A Graphical User Interface (GUI)
provided by MOLA is used. UCSF Chimera version 1.5.321
was used to study and to generate images of the
molecular interactions between docked protein and
ligands molecules.
The molecular docking results of marketed drugs and
glucosylxanthone analogues were presented in table 4.
MOLA tool rank orders each docked ligand based on
predicted binding energy thereby provides results in CSV
format, ligand poses in PDBQ file. Ligand poses for best
runs were obtained from output PDBQT file and
separated out for protein ligand interaction study.
Table 4: Molecular docking results of DPPIV inhibitors
S. No
Molecule
Calculated ∆G Kcal/mole
1
gluxanth3D_1_1
-10.7
2
gluxanth3D_1_9
-10.6
3
gluxanth3D_1_10
-10.5
4
gluxanth3D_1_12
-10.5
5
gluxanth3D_1_2
-10.5
6
gluxanth3D_1_5
-10.5
7
gluxanth3D_1_11
-10.4
8
gluxanth3D_1_4
-10.4
9
gluxanth3D_1_8
-10.4
10
gluxanth3D_1_6
-10.3
11
gluxanth3D_1_7
-10.3
12
gluxanth3D_1_3
-10.2
13
gluxanth3D_1_13
-10.1
14
Linagliptin
-9.6
15
Sitagliptin
-8.7
16
ABT-297
-8.2
17
gluxanth3D_1_14
-8.2
18
Carmegliptin
-7.6
19
Denagliptin
-7.4
20
Saxagliptin
-7.3
21
NVPDPP728
-7.1
22
1X70_ligand
-7.0
23
Vildagliptin
-7.0
24
Alogliptin
-6.9
RESULTS AND DISCUSSION
Problems associated with drug resistance, undesirable
side
effects and emerging diseases making
pharmaceutical scientists life difficult in identifying new
lead molecules and novel scaffolds. Increasing adverse
effects of synthetic drugs renewed the interest of
scientific community towards natural product based drug
discovery. The world Health Organization recognized the
significance of traditional medicine and started working
22
on providing guidelines for plant based medicines.
ISSN 0976 – 044X
Combining the knowledge base of traditional medicine
with modern scientific methods provides better insights
to the drug discovery efforts. Computational chemistry
tools were well established in modern drug discovery that
contributes towards activity profiling with better
strategies, mode of action and in depth details on binding
properties to select true positives in the lead screening
process.
The major objective for anti-diabetic therapy is to reduce
or control glycosylated haemoglobin (HbA1C) levels in
order to minimize complications associated with this
disease such as retinopathy, nephropathy and
neuropathy. Though some of the treatments able to
address glycaemic control due to weight gain and
increased risk of hypoglycaemia, Maintaining glycosylated
haemoglobin (HbA1C) levels <7% as per American Diabetic
Association (ADA) guidelines is becoming difficult despite
various treatments that are currently available emphasis
on the need for better therapy to counteract diabetes
that is prevailing like an iceberg.
Figure 2: Docking pose for Gluxanth3D_1_1 in active site
cavity
To address the problem various novel approaches were
put forth by the scientists and one of such approaches is
Dipeptidyl peptidase inhibitors which can be considered
as second or third line of viable option in type 2 diabetes
management. Inhibition of DPPIV would presumably
increases serum Glucagon like Peptide-1 (GLP-1) that
results in net anti hyperglycaemic effect. Animal studies
conducted to evaluate the impact of DPPIV inhibition
demonstrated improved glucose tolerance and enhance
insulin secretion in Zucker diabetic fatty rats. Hence
competitive, reversible inhibition of DPPIV would be one
of the viable strategies to fight against type 2 diabetes.23
Xanthones represents a group of secondary metabolites
normally found in higher plants, Fungi and lichens.
Glycosylated xanthones were predominant from
Gentianaceae and Polygalaceae families. These molecules
represent a huge family of chemical compounds with
wide diversity of biological and pharmacological
properties. As natural products and traditional medicines
are evolving as attractive options for drug discovery,
traditional Indian medicines like Ayurveda etc. can show
better approach & innovative strategies to drug
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Int. J. Pharm. Sci. Rev. Res., 15(1), 2012; nᵒ 17, 83-87
development process. The advent of using computational
methodologies and tools to the emerging challenges
combined with natural products as a source would
certainly provide better results and the current approach
is outcome of this. The study reveals that
glucosylxanthone and its analogues have shown very
good comparable insilico binding activity with FDA
approved drugs and other molecules currently under
development for DPPIV inhibitory activity. Binding poses
of Auto dock Vina results shows that xanthone
compounds interacts with backbone of GLU205, PHE 357
and ARG367 and is falling in to the definition of lead
24
compound proposed by opera et al.
CONCLUSION
Further optimization of these analogues can serve as
good lead compounds in the development of new DPPIV
inhibitors and the study presented in this article is an
initial effort of the further study in development of
Glucosylxanthone & its analogues as DPPIV inhibitors for
anti -diabetes therapy.
Notes
† Electronic Supplementary Information (ESI) available:
1. ligands files used in this study
2. Protein file used in the study
3. Docking log files
REFERENCES
1 Definition and diagnosis of diabetes mellitus and
intermediate hyperglycemia: Report of a WHO/IDF
Consultation, World Health Organization, 2006, ISBN 92 4
1594934.
2 Harris M, Zimmet P. In Alberti K, Zimmet P, Defronzo R,
editors. International Textbook of Diabetes Mellitus. Second
Edition. Chichester: John Wiley and Sons Ltd; 1997, 9-23.
3 Cassia S Mizuno, Amar G Chittiboyina, Theodore W Kurtz,
Harrihar A Pershadsingh, Mitchell A Avery,Type 2 diabetes
and oral antihyperglycemic drugs, Current Medicinal
Chemistry 2008, 15(1), 61-74.
4 IDF Diabetes Atlas, Fifth Edition, 14th November 2011.
5 Paul E. Wiedeman, DPPIV Inhibition: Promising Therapy for
the Treatment of Type 2 Diabetes,Progress in Medicinal
Chemistry, Edited by F.D. King and G. Lawton, 45, 2007, 63–
109.
6 Marguet, D., Baggio, L., Kobayashi, T., Bernard, A.-M.,
Pierres, M., Nielsen, P.F.,Ribel, U., Watanabe, T., Drucker,
D.J. and Wagtmann, N., Enhanced insulin secretion and
improved glucose tolerance in mice lacking CD26, Proc. Natl.
Acad. Sci.U.S.A. 97, 2000, 6874–6879.
7 Nagakura, T., Yasuda, N., Yamazaki, K., Ikuta, H., Yoshikawa,
S., Asano, O. and Tanaka, I.,Improved Glucose Tolerance via
Enhanced Glucose-Dependent Insulin Secretion in Dipeptidyl
Peptidase IV-Deficient Fischer RatsBiochem. Biophys. Res.
Commun. 284, 2001, 501–506
ISSN 0976 – 044X
8 Bhushan Patwardhan, Ashok D. B. Vaidya and Mukund
Chorghade, Ayurveda and natural products drug discovery,
current science, 86(6), 2004, 789-799.
9 Scartezzini, P. and Speroni, Review on some plants of Indian
traditional medicine with antioxidant activityE.
J.
Ethnopharmacol. 71, 2000, 23–33.
10 ACD/ChemSketch Freeware, version 12.01, Advanced
Chemistry Development, Inc., Toronto, ON, Canada,
www.acdlabs.com, 2012.
11 Arthur Dalby, James G. Nourse, W. Douglas Hounshell, Ann
K. I. Gushurst, David L. Grier, Burton A. Leland, John
Laufer,Description of several chemical structure file formats
used by computer programs developed at Molecular Design
Limited J. Chem. Inf. Comput. Sci., 32 (3), 1992, 244–255,
DOI: 10.1021/ci00007a012.
12 F.C.Bernstein, T.F.Koetzle, G.J.Williams, E.E.Meyer Jr.,
M.D.Brice,
J.R.Rodgers,
O.Kennard,
T.Shimanouchi,
M.Tasumi, The Protein Data Bank: a computer-based
archival file for macromolecular structures, J. of. Mol. Biol.,
112, 1977, 535.
13 Mikko J. Vainio and Mark S. Johnson, Generating Conformer
Ensembles Using a Multiobjective Genetic Algorithm, Journal
of Chemical Information and Modeling, 47, 2007, 2462 –
2474.
14 Noel M O'Boyle, Michael Banck, Craig A James,Chris Morley,
Tim Vandermeersch and Geoffrey R Hutchison, Open Babel:
An open chemical toolbox, Journal of Cheminformatics 3,
2011, 33, doi:10.1186/1758-2946-3-33.
15 Thomas A. Halgren, Merck molecular force field. I. Basis,
form, scope, parameterization, and performance of
MMFF94, J. Comp. Chem.; 1996; 490-519.
16 Kim D, Wang L, Beconi M, Eiermann GJ, Fisher MH, He H,
Hickey GJ, Kowalchick JE, Leiting B, Lyons K, Marsilio F,
McCann ME, Patel RA, Petrov A, Scapin G, Patel SB, Roy RS,
Wu JK, Wyvratt MJ, Zhang BB, Zhu L, Thornberry NA, Weber
AE.,
(2R)-4-oxo-4-[3-(trifluoromethyl)-5,6dihydro[1,2,4]triazolo[4,3-a]pyrazin-7(8H)-yl]-1-(2,4,5trifluorophenyl)butan-2-amine: a potent, orally active
dipeptidyl peptidase IV inhibitor for the treatment of type 2
diabetes, J Med Chem. 48(1), 2005 Jan 13, 141-51.
17 Michel F. Sanner, Python: A Programming Language for
Software Integration and Development, J. Mol. Graphics
Mod., 17, 1999, 57-61.
18 Rui MV Abreu, Hugo JC Froufe, Maria JRP Queiroz and Isabel
CFR Ferreira, MOLA: a bootable, self-configuring system for
virtual screening using AutoDock4/Vina on computer
clusters, Journal of Cheminformatics 2, 2010, 10.
19 Morris, G. M., Goodsell, D. S., Halliday, R.S., Huey, R., Hart,
W. E., Belew, R. K. and Olson, A. J., Automated Docking
Using a LamarckianGenetic Algorithm and and Empirical
Binding Free Energy Function, J. Computational Chemistry,
19, 1998, 1639-1662.
20 O. Trott, A. J. Olson,AutoDock Vina: Improving the speed and
accuracy of docking with a new scoring function, efficient
optimization, and multithreading Journal of Computational
Chemistry 31, 2010, 455-461.
21 Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt
DM, Meng EC, Ferrin TE., UCSF Chimera--a visualization
International Journal of Pharmaceutical Sciences Review and Research
Available online at www.globalresearchonline.net
Page 86
Int. J. Pharm. Sci. Rev. Res., 15(1), 2012; nᵒ 17, 83-87
ISSN 0976 – 044X
system for exploratory research and analysis,J Comput
Chem. 25(13), 2004 Oc, 1605-12.
Occurrence of Diabetes in Male Zucker Diabetic Fatty Rats,
Diabetes 51, 2002, 1461– 1469.
22 Traditional Medicine Strategy 2002–2005, WHO, Geneva,
2002.
24 Tudor I. Oprea, Andrew M. Davis, Simon J. Teague, and Paul
D. Leeson, Is There a Difference between Leads and Drugs?
A Historical Perspective J. Chem. Inf. Comput. Sci., 41(5),
2001, 1308–1315.
23 Sudre B, Broqua P, White RB:, Chronic Inhibition of
Circulating Dipeptidyl PeptidaseIV by FE 999011 Delays the
About Corresponding Author: Mr. K.V.T.S. Pavan Kumar
K.V.T.S. Pavan Kumar obtained his Masters in biochemistry. He specializes in Bio-informatics,
Trascriptome profiling for characterization of novel genes and in silico-biology. Currently working
as a researcher in the field of Plant based natural products with Dalmia Centre for Research and
Development, Coimbatore.
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